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Abstract Solar active regions (ARs) contain a broad range of temperatures, with the thermal plasma distribution often observed to peak in the few millions of kelvin. Differential emission measure (DEM) analysis can allow instruments with diverse temperature responses to be used in concert to estimate this distribution. Nuclear Spectroscopic Telescope ARray (NuSTAR) hard X-ray (HXR) observations are uniquely sensitive to the highest-temperature components of the corona, and thus extremely powerful for examining signatures of reconnection-driven heating. Here, we use NuSTAR diagnostics in combination with extreme-ultraviolet and soft X-ray observations (from the Solar Dynamics Observatory/Atmospheric Imaging Assembly and Hinode/X-Ray Telescope) to construct DEMs over 170 distinct time intervals during a 5 hr observation of an alternately flaring and quiet active region (NOAA designation AR 12712). This represents the first HXR study to examine the time evolution of the distribution of thermal plasma in an AR. During microflares, we find that the initial microflare-associated plasma heating is predominantly heating of material that is already relatively hot, followed later on by broader heating of initially cooler material. During quiescent times, we show that the amount of extremely hot (>10 MK) material in this region is significantly (∼2–4 orders of magnitude) less than that found in the quiescent AR observed in HXRs by FOXSI-2. This result implies there can be radically different high-temperature thermal distributions in different ARs, and strongly motivates future HXR DEM studies covering a large number of these regions. 

One mechanism giving fleshy algae a competitive advantage over corals during reef degradation is algal-induced and microbially-mediated hypoxia (typically less than 69.5 µmol oxygen L −1 ). During hypoxic conditions oxygen availability becomes insufficient to sustain aerobic respiration in most metazoans. Algae are more tolerant of low oxygen conditions and may outcompete corals weakened by hypoxia. A key question on the ecological importance of this mechanism remains unanswered: How extensive are local hypoxic zones in highly turbulent aquatic environments, continuously flushed by currents and wave surge? To better understand the concert of biological, chemical, and physical factors that determine the abundance and distribution of oxygen in this environment, we combined 3D imagery, flow measurements, macro- and micro-organismal abundance estimates, and experimentally determined biogenic oxygen and carbon fluxes as input values for a 3D bio-physical model. The model was first developed and verified for controlled flume experiments containing coral and algal colonies in direct interaction. We then developed a three-dimensional numerical model of an existing coral reef plot off the coast of Curaçao where oxygen concentrations for comparison were collected in a small-scale grid using fiberoptic oxygen optodes. Oxygen distribution patterns given by the model were a good predictor for in situ concentrations and indicate widespread localized differences exceeding 50 µmol L -1 over distances less than a decimeter. This suggests that small-scale hypoxic zones can persist for an extended period of time in the turbulent environment of a wave- and surge- exposed coral reef. This work highlights how the combination of three-dimensional imagery, biogenic fluxes, and fluid dynamic modeling can provide a powerful tool to illustrate and predict the distribution of analytes (e.g., oxygen or other bioactive substances) in a highly complex system. 

The goal of the SunPy project is to facilitate and promote the use and development of community-led, free, and open source data analysis software for solar physics based on the scientific Python environment. The project achieves this goal by developing and maintaining the sunpy core package and supporting an ecosystem of affiliated packages. This paper describes the first official stable release (version 1.0) of the core package, as well as the project organization and infrastructure. This paper concludes with a discussion of the future of the SunPy project.